



"""simple docstring"""
import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

SCREAMING_SNAKE_CASE_      						=			3



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__						)      ->   int:
		print("Generating primitive root of p"						)
		while True:
				a_  :   str			    =  random.randrange(3,			_lowerCamelCase						)
				if pow(_lowerCamelCase,			2,			_lowerCamelCase						) == 1:
						continue
				if pow(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase						) == 1:
						continue
				return g



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__						)      ->   tuple[tuple[int, int, int, int], tuple[int, int]]:
		print("Generating prime p..."						)
		a_  :   Any			    =  rabin_miller.generate_large_prime(_lowerCamelCase						)  # select large prime number.
		a_  :   Dict			    =  primitive_root(_lowerCamelCase						)  # one primitive root on modulo p.
		a_  :   Tuple			    =  random.randrange(3,			_lowerCamelCase						)  # private_key -> have to be greater than 2 for safety.
		a_  :   List[str]			    =  cryptomath.find_mod_inverse(pow(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase						),			_lowerCamelCase						)

		a_  :   Any			    =  (key_size, e_a, e_a, p)
		a_  :   Any			    =  (key_size, d)

		return public_key, private_key



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__						)      ->   None:
		if os.path.exists(F"""{name}_pubkey.txt"""						) or os.path.exists(F"""{name}_privkey.txt"""						):
				print("\nWARNING:"						)
				print(
				    F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
				    "Use a different name or delete these files and re-run this program."						)
				sys.exit()

		a_    ,					a_  :   Tuple			    =  generate_key(_lowerCamelCase						)
		print(F"""\nWriting public key to file {name}_pubkey.txt..."""						)
		with open(F"""{name}_pubkey.txt""",			"w"						) as fo:
				fo.write(F"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""						)

		print(F"""Writing private key to file {name}_privkey.txt..."""						)
		with open(F"""{name}_privkey.txt""",			"w"						) as fo:
				fo.write(F"""{private_key[0]},{private_key[1]}"""						)



def 							lowerCAmelCase_    (				)      ->   None:
		print("Making key files..."						)
		make_key_files("elgamal",			2_048						)
		print("Key files generation successful"						)


if __name__ == "__main__":
					main()







"""simple docstring"""





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_SCREAMING_SNAKE_CASE   :							int          =		pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		dataset.iloc[:, 1:2].values
_SCREAMING_SNAKE_CASE   :							str          =		dataset.iloc[:, 2].values


_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE   :							List[str]          =		LinearRegression()
pol_reg.fit(X_poly, y)







def 	lowerCamelCase__			(				) ->							List[str]:
  plt.scatter(_lowerCamelCase						,							_lowerCamelCase						,							color='red'					)
  plt.plot(_lowerCamelCase						,							pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase					)					)						,							color='blue'					)
  plt.title('Truth or Bluff (Linear Regression)'					)
  plt.xlabel('Position level'					)
  plt.ylabel('Salary'					)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003




